Responsible tech: a critical consideration

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Building ethical guardrails

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As technology has become more deeply interwoven in various parts of everyday life, the possibility of harm — both intentional and inadvertent — becomes more acute. The ongoing debate about the far-reaching implications of generative AI (GenAI) is just one example of how focus on the potential fallout from technology solutions, whether in the form of misinformation, excessive carbon emissions or the exclusion of certain groups, is increasing. According to our research on what consumers want from GenAI, 93% of those surveyed say businesses which fail to incorporate responsible and ethical thinking risk detrimental impacts.

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Organizations must be prepared for their technology practices to come under more scrutiny and think through the ethical ramifications of their technology choices — not just for end-users, but for society as a whole.��

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Responsible technology ensures consideration for all stakeholders, as well as guardrails around privacy, security and sustainability, are firmly embedded in the organization’s technology approach.��

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As leaders we must recognize that we often struggle to accurately predict the consequences of our technology choices. More often than not the negative effects of technology are unintentional — but that makes them no less harmful. Practicing responsible technology is a matter of broadening our perspectives and taking advantage of emerging tools and techniques that can support enterprises on their responsible tech journey, from secure software to privacy-first design.��

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Responsible tech is not just about being aware of what could happen as a result of our well-intentioned actions. It’s about being fully engaged with the now — by constantly reevaluating who and what we’re protecting and how we’re doing it; we’re never done.

Rebecca Parsons

Chief Technology Officer Emerita, ��

Responsible tech is not just about being aware of what could happen as a result of our well-intentioned actions. It’s about being fully engaged with the now — by constantly reevaluating who and what we’re protecting and how we’re doing it; we’re never done.

Rebecca Parsons

Chief Technology Officer Emerita, ��

Signals

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New resources to define and provide education on responsible technology practices, such as the��responsible technology playbook��developed jointly by �� and the United Nations, as well as the Social and Ethical Responsibilities of Computing curriculum developed by MIT, available via the institution’s��platform.��

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Regulatory and policy initiatives designed to mandate more considered approaches to technology, such as the US��on the Safe, Secure, and Trustworthy Development and US of Artificial Intelligence, India’s��, and the EU’s��and connected impact assessment.��

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The emergence of alliances focused on developing and promulgating sustainable technology practices. The��which has developed training and code for less carbon-intensive software and methodologies to calculate the emissions associated with technology, is one prominent example.��

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The birth of investment funds targeting responsible technology companies and solutions, such as��, which is channeling $35 million to early-stage startups working in privacy, decentralizing digital power and ethical AI.��

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An uptick in firms promoting responsible technology principles and credentials, including giants like��,��and��.��

Strategic recommendation

Adopt

AI is increasingly being deployed both defensively, to respond to threats more dynamically, and offensively, to probe for weaknesses in a system.
The use of AI to speed up or improve software development. Examples include code completion in IDEs, AI-created automated tests, AI that can detect bugs or even AI code generation tools.
The use of technology to make all the data required to satisfy compliance reports, checks and balances readily available. In many cases, the automation simplifies reporting by sifting through data; however, AI is now beginning to replace manual decision making.
A set of guidelines organizations can use to manage risk and mitigate the potential negative consequences of given technologies (such as AI bias).
Rather than using traditional security perimeters that are a single point of failure, techniques such as zero-trust networks decentralize security checks across the network.
An abbreviated portmanteau for development, security and operations. This is an approach that includes security as a first-class concern, together with development and operations.
Measuring organizational green house gas (GHG) emissions and efforts to mitigate those emissions. Establishing a carbon footprint and a program to determine it is an essential component on the journey towards net zero and is the first building block towards any sustainability strategy.
A category of electrical power generation that are “behind-the-meter.” DERs generate power for the grid, and reward energy credits to the DER owner. An example is solar panels installed on a home.
Bringing data storage and processing closer to the devices where it is stored, rather than relying on a central location that may be thousands of miles away. Benefits include reduced latency for real-time systems and improved data privacy.
Decision-making frameworks that attempt to bring transparency and clarity into the way decisions are made, especially around the use of AI and potential bias in data.
Data centers fed by renewable energy, running software and systems designed and optimized for efficient processing while also minimizing energy consumption.
Choosing technologies, programming languages, algorithms and software architectures that are efficient and use less energy.
Privacy first is a significant shift in business, organization and product strategy, where privacy operates as a core business value and offering. This shift moves away from the prior movement where "users are the product", into a new realm, where building trust and transparency comes first.
A method in artificial intelligence where the system enhances its response generation by fetching relevant information from a large database or knowledge source. This approach combines the creative aspects of generative AI models with the precision of data retrieval, enabling more accurate and contextually relevant responses in various business applications.
Security applied to the entire process of software creation, which in modern architectures includes the delivery pipeline used to build, test and deploy applications and infrastructure.
Featuring smart hubs, homes are now becoming 'smart', allowing people to control almost all household systems. Analytics can even guide or manage heat and energy supply and learn from individual habits or those in a neighbourhood.
Specialized storage systems designed to efficiently handle and index high-dimensional data vectors, commonly used in machine learning and AI applications.

Analyze

Images, audio or video that have been manipulated by AI. Also known as synthetic media.
Currencies other than money, such as cryptocurrencies or reputation-based currency. Increasingly, this includes vendor-specific reward-based currencies such as Starbucks Stars or Amazon Coins.
The use of technology to perform repeatable or predictable workflows. Automated workforce doesn’t mean completely replacing humans; in some cases human-machine "teaming" may produce better results than either working alone.
A system that enables the finding, buying, sharing and selling of data within and outside an organization.
A privacy technique that introduces noise in a dataset in such a way as to provide individual privacy while still allowing insights to be drawn or machine learning models to be built on top of the data.
The ability to perform calculations on encrypted data, without first decrypting it. Useful to maintain data privacy while allowing data storage and manipulation to be outsourced. This includes technologies like secure multi-party computation and homomorphic encryption.
An urban area that uses different types of IoT sensors to collect data coupled with platforms to integrate and act on the data, advising or commanding digitally enabled systems to perform some response. Insights gained from the data are used to manage assets, resources and services efficiently; in return, that data is used to improve the operations across the city.
Ubiquitous availability of energy usage data via measurement equipment, APIs and tools gives a range of energy players (generators, distributors, suppliers, vendors) and customers a greater ability to understand and analyze their energy usage.
Rising forces are leading to internet balkanization — the splintering of the internet — many led by nation states. Privacy legislation accelerates this process, as it enforces data rights, data sovereignty, and strongly impacts how companies deploy and distribute systems and data on the Internet.
A method that allows one party to prove to another that a statement is true without revealing how it knows it is true.

Anticipate

Some applications are specifically designed to be addictive, driven by fierce competition for eyeballs and engagement. While this might be good for companies in the business of selling advertisements to audiences, there is an increasing awareness of the societal and environmental harms of addictive tech.
Crypto assets are traded across the world. Similar to the move for international laws for AI, crypto assets also need international law for cross border trading. This might include costs, categories of assets and what constitutes legal trading

Adopt

Also known as self-sovereign identity, decentralized identity (DiD) is an open-standards-based identity architecture that uses self-owned and independent digital IDs and verifiable credentials to transmit trusted data. Although not dependent on blockchains, many current examples are deployed on them as well as other forms of distributed ledger technology, and private/public key cryptography, it seeks to protect the privacy of and secure online interactions.
More granular access controls for data, such as policy-based (PBAC) or attribute-based (ABAC) that can apply more contextual elements when deciding who has access to data.

Analyze

Government regulation and guidance on the use of AI, intended to ensure responsible use and consequences of AI systems. This includes monitoring, compliance and good practice.
Combines AI tools and techniques with behavioral and management sciences for the purpose of upskilling and amplifying decision making and decision makers across a variety of complex problems from scenario planning to operations research.
AI-powered virtual assistants and non-playable characters that recreate human interaction within the metaverse.
Design of user interfaces and prompts that help people understand the environmental consequences of the choices they make. Examples include an airline website displaying carbon emissions for flights or a mapping tool showing the carbon output for driving a particular route.
Systems that monitor metrics across complex distributed systems and take corrective action if a problem is detected. They are often used for security, but increasingly also for resilience and recovery in the face of an outage.
An emerging set of techniques to certify the provenance of data and to govern its use across an organization. This could prove transformative in the effort to track and enhance progress towards sustainability targets.

Anticipate

These are attacks on (or using) machine learning systems. Attackers may tamper with training data or identify specific inputs that a model classifies poorly to deliberately create undesired outcomes.
A collective term for systems and devices that can recognize, interpret, process, simulate and respond to human emotions.
Use of probabilistic states of photons, rather than binary ones and zeros, to run algorithms. Although proven to work in specific problem spaces, quantum computing has yet to scale to broadly useful applications.
Tools and techniques are emerging that support incorporating responsible tech into software delivery processes, primarily focusing on actively seeking to incorporate under-represented perspectives; some examples include Tarot Cards of Tech, Consequence Scanning, and Agile Threat Modeling.
A closed economic system where raw materials and products are constantly shared so as to lose their value as little as possible. Technology that supports this includes reusable services, traceability, IoT and data mining.
Most terms of service (TOS) or end-user license agreements (EULAs) are impenetrable legalese that make it difficult for people without a law background to understand. Understandable consent seeks to reverse this pattern, with easy-to-understand terms and clear descriptions of how customers' data will be used.

Adopt

No trends

Analyze

No trends

Anticipate

An Artificial General Intelligence (AGI) has broad capabilities across a range of intellectual tasks, and is often compared to human-level intelligence. This contrasts with today's "narrow" AI which can be remarkable, but only for very specific tasks.
A data architecture style where individuals control their own data in a decentralized manner, allowing access on a per-usage bases (for example, Solid PODs).
Forms of cryptography created in response to technological or societal challenges. Examples include quantum-resistant encryption algorithms, confidential computing with specialized hardware secure enclaves, homomorphic encryption allowing computation to occur on the data while it is still encrypted, and energy efficient cryptography.
Machine learning algorithms adapted and executed on a quantum computing engine, generally used to analyze classical (non-quantum) data.

The opportunities

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By getting ahead of the curve on this lens, organizations can:

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Prevent reputational damage among customers, talent and investors when technology-driven ethical lapses enter the public sphere. Beyond mitigating harm, an effective responsible technology practice can pay dividends in terms of customer and talent attraction and retention. One recent survey of millennial and Gen Z workers, for example, found they place high priority on employers being .

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Avoid regulatory scrutiny or sanctions, such as those faced by Apple when apparent bias in the algorithm that sets Apple Card spending limits .

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Reduce the likelihood of data breaches or misuse. Cases such as and, more recently, have proven these incidents come with punishing costs that can drag on for years.��

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Generate positive environmental outcomes. Efforts to measure and reduce the carbon intensity of computing and cloud usage through tools like ��’ open-source (CCF) open the door to aligning technology with the organization’s overall sustainability strategy and the path to net zero.

The Responsible Tech Playbook, developed by �� and the UN

What we’ve done

Responsible tech playbook with the UN

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In line with the , the United Nations Secretariat worked together with �� to provide guidance on ensuring inclusivity, awareness of bias, transparency and the mitigation of negative unintended consequences in examining emerging technologies, including generative artificial intelligence (GenAI). Following a series of interviews and workshops with a wide range of United Nations staff, the �� and UN team developed a framework and set of approaches for the responsible creation and management of technology systems and products.

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Actionable advice

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Things to do (Adopt)

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Treat responsible tech practices as a cross-functional requirement. As mentioned in the title of this lens, including ethical considerations is critical to all organizations.

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Continuously update technology planning and processes to incorporate techniques and exercises that help map out the broader consequences of solutions you apply or develop — for example by involving underrepresented groups in design and testing, or simulating breaches that show how�� data could be misused. Make these techniques part of every process.��

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Establish clear guardrails and policies governing the use of AI and ensure these are communicated not only to technologists, but to other parts of the organization where more people will be experimenting with AI tools in their day-to-day roles.

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Adopt secure software delivery practices, such as making secure development a collective responsibility, producing clean, transparent and easily maintainable code, and continuous testing.��

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Examine your software development processes and tools to understand where you can make more sustainable decisions. Understand the cost / benefit trade offs of green software engineering techniques.��

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Be a good consumer by taking steps to understand your partners’ and suppliers’ stances on responsible technology, and making efforts to engage and support organizations that demonstrate commitment to ethical technology usage

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Things to consider (Analyze)

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Developing trustworthy data sources by examining the provenance of information; gathering data where possible from providers that have been vetted and forging partnerships with trusted organizations in your space that govern data-sharing and exchange.

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Constantly considering changes to what constitutes responsible technology. Technology is quickly evolving; the problematic activities of tomorrow may not even be possible with the technology of today.

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Adopting a code of ethics for software development, either by developing principles that are customized to your organization, or by building on or promoting pioneering standards like the .��

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Utilizing green software development techniques such as implementing real-time power consumption monitoring to keep emissions to the minimum viable level, optimizing infrastructure and algorithms and carefully selecting both the location and timing of computation.��

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Things to watch for (Anticipate)

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Evolving opportunities and threats from developments in AI. As the frontiers of what is possible for AI to create — or manipulate — rapidly expand, it promises to make significant contributions to everything from market research to product development. However, it will also vastly accelerate the scale and reach of destructive forces like deepfakes and misinformation campaigns. Ensure your organization remains cognizant of and prepared for the new dilemmas AI will present even as you take advantage of its capabilities.��

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Evolving regulations. You should expect to see regulatory changes across the entire gamut of responsible technology areas. Educate your compliance organizations about new regulatory bodies or agencies which need to be monitored and potentially engaged with across the broad areas we’ve covered here.

Lens one: AI everywhere

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Lens two: Realizing value from data and AI platforms

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Lens three: Evolving interactions

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Lens four: Accelerating physical-digital convergence

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